Austin et al., "How to Scale Your Model", Google DeepMind, online, 2025.
Ansel et al. 2024. “PyTorch 2: Faster Machine Learning through Dynamic Python Bytecode Transformation and Graph Compilation.” ACM, April.
Baydin et al. 2015. “Automatic Differentiation in Machine Learning: A Survey.” ArXiv.org
Bakhvalov, Denis. Performance Analysis and Tuning on Modern CPUs. 2024.
Bright, Paige, Alan Edelman, and Steven G Johnson. 2025. “Matrix Calculus (for Machine Learning and Beyond).” ArXiv.org
Bryant, Randal E, and David R O’hallaron. 2016. Computer Systems: A Programmer’s Perspective. Boston: Pearson Education.
Blondel, Mathieu, and Vincent Roulet. 2024. “The Elements of Differentiable Programming.” ArXiv.org
Boehm, Simon. 2022. “How to Optimize a CUDA Matmul Kernel for cuBLAS-like Performance: A Worklog.” Siboehm.com
Chen, Tianqi, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, et al. 2018. “TVM: An Automated End-To-End Optimizing Compiler for Deep Learning.” ArXiv, February.
Fog, Agner. “Software Optimization Resources. C++ and Assembly. Windows, Linux, BSD, Mac OS X.” Agner.org.
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. Cambridge, Massachusetts: The MIT Press.
Griewank, Andreas, and Andrea Walther. 2009. Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation. Philadelphia, Pa.: Society For Industrial & Applied Mathematics ; Cambridge.
Hastie, Trevor, et al. The Elements of Statistical Learning, Second Edition : Data Mining, Inference, and Prediction. 2nd ed., New York, Springer, 2009.
Hennessy, John L, and David A Patterson. 2025. Computer Architecture: A Quantitative Approach. Cambridge, Ma: Morgan Kaufmann.
Hwu, Wen-Mei W, David B. Kirk, and Izzat El Hajj. 2022. Programming Massively Parallel Processors: A Hands-on Approach. S.L.: Morgan Kaufmann.
Jurafsky, Dan, and James H. Martin. 2025. “Speech and Language Processing.” Stanford.edu. 2025.
Murphy, Kevin P. 2023. Probabilistic Machine Learning: Advanced Concepts. MIT Press.
Murphy, Kevin P. 2022. Probabilistic Machine Learning: An Introduction. Cambridge: MIT Press.
Paszke, Adam, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, et al. 2019. “PyTorch: An Imperative Style, High-Performance Deep Learning Library.” ArXiv.org
Ragan-Kelley, Jonathan, Connelly Barnes, Andrew Adams, Sylvain Paris, Frédo Durand, and Saman Amarasinghe. 2013. “Halide.” Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation.
Shankhdhar, Pranjal. 2025. “Outperforming cuBLAS on H100: A Worklog.” 2025.
Suhan, Alex, Davide Libenzi, Ailing Zhang, Parker Schuh, Brennan Saeta, Jie Young Sohn, and Denys Shabalin. 2021. “LazyTensor: Combining Eager Execution with Domain-Specific Compilers.” ArXiv.org
Sutton, Richard S, and Andrew Barto. Reinforcement Learning: An Introduction. 2nd ed., Cambridge, Ma ; London, The Mit Press, 2018.
Tazi et al., "The Ultra-Scale Playbook: Training LLMs on GPU Clusters", 2025.
Uwe Naumann. 2012. The Art of Differentiating Computer Programs: An Introduction to Algorithmic Differentiation. Philadelphia: Society For Industrial And Applied Mathematics.